Gaál G
Department of Physiology, University of Pennsylvania, PA 19104.
J Theor Biol. 1993 May 7;162(1):103-30. doi: 10.1006/jtbi.1993.1079.
In this work we carried out computer simulations to compare different coding algorithms (the tensor network theoretical approach of Pellionisz & Llinas, 1979; and the weighted average population coding model of Georgopoulos et al., 1986) that were originally devised to recompute vectors of the external world from firing rate responses of neurons of the central nervous system. Georgopoulos and his colleagues (1986, 1988) observed, in electrophysiological experiments, that certain neurons of the primate motor cortex are selective to the direction of arm movement in a three-dimensional space (directional neurons). The discharge rate of a cell is highest with movement in a certain direction (the cell's "preferred direction") and decreases as a linear function of the cosine of the angle between the direction of movement and the cell's preferred direction. They calculated a population vector to predict the direction of arm movement from neuronal responses as a weighted linear combination of the preferred direction vectors using several sets of weighting coefficients. It was implicitly assumed in this approach that if the brain uses such coding, the calculations are carried out by a further layer of neurons. The tensor network theory also gives an algorithm to calculate an external vector in an intrinsic co-ordinate system whose basis vectors are distinguished vectors assigned to the individual neurons based on results of physiological observations. However, it goes beyond providing a simple mathematical formula to recompute an external vector (Pellionisz & Llinas, 1979). It is a promising theoretical solution for the problem faced by sensorimotor systems; how to transform information about the environment, measured by a diverse set of sensors, into appropriate responses executed by multiple muscles acting in concert. As the weighting coefficients used by Georgopoulos et al. in their calculations differed from those used by Pellionisz & Llinas, we show here how they are related. We compared the exactness and robustness of the two approaches in computer simulations assuming that the firing rate responses of individual neurons would change from trial to trial even when the movement direction is the same. We also allowed that different sets of preferred directions were used in different trials mimicking the case when different movement-related directional neurons would be active from trial to trial. In our computer simulations the outcome of the different algorithms were fairly similar. No experimental protocol can be devised in which the activity of all the possible active motor cortex neurons taking part in coding the direction of movement could be simultaneously monitored.(ABSTRACT TRUNCATED AT 400 WORDS)
在这项工作中,我们进行了计算机模拟,以比较不同的编码算法(佩利奥尼斯和利纳斯1979年提出的张量网络理论方法;以及乔治opoulos等人1986年提出的加权平均群体编码模型),这些算法最初旨在根据中枢神经系统神经元的放电率响应重新计算外部世界的向量。乔治opoulos及其同事(1986年、1988年)在电生理实验中观察到,灵长类动物运动皮层的某些神经元对三维空间中手臂运动的方向具有选择性(方向神经元)。细胞的放电率在某个特定方向(细胞的“偏好方向”)的运动时最高,并随着运动方向与细胞偏好方向之间夹角的余弦的线性函数而降低。他们计算了一个群体向量,以根据神经元的反应预测手臂运动的方向,该群体向量是使用几组加权系数对偏好方向向量进行加权线性组合得到的。在这种方法中隐含地假设,如果大脑使用这种编码,计算是由另一层神经元进行的。张量网络理论也给出了一种算法,用于在一个内在坐标系中计算一个外部向量,该坐标系的基向量是根据生理观察结果分配给各个神经元的独特向量。然而,它不仅仅是提供一个简单的数学公式来重新计算外部向量(佩利奥尼斯和利纳斯,1979年)。它是解决感觉运动系统所面临问题的一个有前途的理论方案;即如何将由各种传感器测量的关于环境的信息转化为由协同作用的多块肌肉执行的适当反应。由于乔治opoulos等人在计算中使用的加权系数与佩利奥尼斯和利纳斯使用的不同,我们在此展示它们之间的关系。我们在计算机模拟中比较了这两种方法的准确性和稳健性,假设即使运动方向相同,单个神经元的放电率响应在不同试验中也会发生变化。我们还允许在不同试验中使用不同的偏好方向集,模拟不同试验中不同的与运动相关的方向神经元会被激活的情况。在我们的计算机模拟中,不同算法的结果相当相似。无法设计出一种实验方案,能够同时监测参与编码运动方向的所有可能活跃的运动皮层神经元的活动。(摘要截短至400字)